Genomic evaluation of late-term abortion in cows recorded through Dairy Herd Improvement test plans.

Late-term abortions cause significant economic loss and are of great concern for dairy herds. Late-term abortions >152 d and <251 d of gestation that terminate a lactation or initiate a new lactation have long been recorded by Dairy Herd Improvement (DHI). For 24.8 million DHI lactations, the average recorded incidence of late-term abortions across all years (2001-2018) was 1.2%. However, the 1.3% incidence of abortions reported in 2012 has declined to <1.0% incidence since 2015. Small adjustments were applied to the 82 million daughter pregnancy rate (DPR), 29 million cow conception rate (CCR), and 9 million heifer conception rate (HCR) records to account for late-term abortions more accurately. Fertility credits for CCR and HCR were changed to treat the last breeding as a failure instead of success if the next calving was coded as a late-term abortion. Similarly, when computing DPR, days open is now set to the maximum value of 250 instead of the reported days open if the next reported calving is an abortion. The test of these changes showed very small changes in standard deviation and high correlations (0.997) of adjusted predicted transmitting abilities (PTA) with official PTA from about 20,000 Holstein bulls born since 2000 with >50% reliability. For late-term abortion as a trait, estimated heritability was only 0.001 and PTA had a standard deviation of only 0.1% for recent sires with high reliability (>75%). Young animal genomic PTA have near 50% reliability but range only from -0.5 to +0.4 because of the low incidence and heritability. Genetic trend was slightly favorable and late-term abortion PTA were correlated favorably by 0.27 with net merit, 0.49 with productive life, 0.33 with livability, 0.23 with CCR, 0.20 with HCR, 0.26 with DPR, -0.31 with somatic cell score, -0.24 with daughter stillbirth, and -0.26 with daughter dystocia. Thus, PTA for late-term abortions should not be needed as a separate fertility trait and instead these minor edit changes should suffice. The PTA for late-term abortions would add little value because national evaluations for current fertility traits already account for those economic losses.

Changes in genetic trends in US dairy cattle since the implementation of genomic selection.

Genomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Mean genomic predicted breeding values (PBV¯) were analyzed per year to calculate genetic trends for bulls and cows. The data set contained 154,008 bulls and 33,022,242 cows born since 1975. Breakpoints were estimated using linear regression, and nonlinear regression was used to fit the piecewise model for the small sample number in some years. Generation intervals and inbreeding levels were also investigated since 1975. Milk, fat, and protein yields, somatic cell score, productive life, daughter pregnancy rate, and livability PBV¯ were documented. In 2017, 100% of bulls in this data set were genotyped. The percentage of genotyped cows has increased 23 percentage points since 2010. Overall, production traits have increased steadily over time, as expected. The HO and JE breeds have benefited most from genomics, with up to 192% increase in genetic gain since 2009. Due to the low number of observations, trends for Ayrshire, Brown Swiss, and Guernsey are difficult to infer from. Trends in fertility are most substantial; particularly, most breeds are trending downwards and daughter pregnancy rate for JE has been decreasing steadily since 1975 for bulls and cows. Levels of genomic inbreeding are increasing in HO bulls and cows. In 2017, genomic inbreeding levels were 12.7% for bulls and 7.9% for cows. A suggestion to control this is to include the genomic inbreeding coefficient with a negative weight to the selection index of bulls with high future genomic inbreeding levels. For sires of bulls, the current generation intervals are 2.2 yr in HO, 3.2 in JE, 4.4 in Brown Swiss, 5.1 in Ayrshire, and 4.3 in Guernsey. The number of colored breed bulls in the United States is currently at an extremely low level, and this number will only increase with a market incentive or additional breed association involvement. Increased education and extension could be beneficial to increase knowledge about inbreeding levels, use of genomics and genetic improvement, and genetic diversity in the genomic selection era.

Genomic evaluation of dairy heifer livability.

Differences in breeds and sire lines suggest the presence of a genetic component for heifer livability (HLIV). Genomic evaluation for this trait can increase profitability and improve animal health and welfare. Evaluations for HLIV were examined from 3,362,499 calf data records from heifers of all breeds born from 2009 to 2016. Data were obtained from the national cooperator database maintained by the Council on Dairy Cattle Breeding (https://www.uscdcb.com/). The total number of deaths reported was 134,753 (4.01%), which included herds with death loss between 1.5 and 25.5%. Age at death was evaluated and ranged from >2 d of age until the heifer left the herd, with a maximum of 18 mo of age. Records were not included until 3 yr after the birthdate so that live status of contemporaries could be confirmed by a calving date for those animals. Deaths observed until 2 d after birth were considered to be a stillbirth rather than a failure of HLIV. The scale used for analysis of HLIV was 0 (died) or 100 (live), and the heritability estimate was 0.7% based on sire model with restricted maximum likelihood estimation. Genomic predicted transmitting abilities for Holstein ranged from -1.6% to +1.6% with a standard deviation of 0.5%, and genomic predicted transmitting abilities for Jersey ranged from -0.5% to +0.5% with a standard deviation of 0.2%. The mean overall death loss was about 4%. Reliabilities of genomic predictions for young animals averaged 46% for Holsteins and 30% for Jerseys, and corresponding traditional parent average reliabilities averaged 16% and 12%, respectively. Correlations of HLIV were 0.44 with productive life, 0.18 to 0.22 with yield traits, and 0.29 with early first calving on proven Holstein bulls. The HLIV trait had a favorable genetic trend in recent years, likely because of the indirect selection associated with the correlated traits. The trait HLIV should receive 1% of emphasis on the Lifetime Net Merit index, resulting in economic progress worth $50,000/yr. By encouraging more comprehensive recording on calf mortality, the reliabilities of genetic predictions could increase significantly.

Defining the optimal period length and stage of growth or lactation to estimate residual feed intake in dairy cows.

Residual feed intake (RFI) is an estimate of animal feed efficiency, calculated as the difference between observed and expected feed intake. Expected intake typically is derived from a multiple regression model of dry matter intake on energy sinks, including maintenance and growth in growing animals, or maintenance, gain in body reserves, and milk production in lactating animals. The best period during the production cycle of a dairy cow to estimate RFI is not clear. Here, we characterized RFI in growing Holstein heifers (RFIGrowth; ∼10 to 14 mo of age; n = 226) and cows throughout a 305-d lactation (RFILac-Full; n = 118). The goals were to characterize relationships between RFI estimated at different production stages of the dairy cow; determine effects of selection for efficiency during growth on subsequent lactation and feed efficiency; and identify the most desirable testing scheme for RFILac-Full. For RFIGrowth, intake was predicted from multiple linear regression of metabolizable energy (ME) intake on mid-test body weight (BW)0.75 and average daily gain (ADG). For RFILac-Full, predicted intake was based on regression of BW0.75, ADG, and energy-corrected milk yield. Mean energy intake of the least and most efficient growing heifers (±0.5 standard deviations from mean RFIGrowth of 0) differed by 3.01 Mcal of ME/d, but the groups showed no difference in mid-test BW or ADG. Phenotypic correlation between RFIGrowth and RFI of heifers estimated in the first 100 d in milk (RFILac100DIM; n = 130) was 0.37. Ranking of these heifers as least (mean + 0.5 standard deviations), middle, or most efficient (mean - 0.5 standard deviations) based on RFIGrowth resulted in 43% maintaining the same ranking by RFILac100DIM. On average, the most efficient heifers ate 3.27 Mcal of ME/d less during the first 100 DIM than the least efficient heifers, but exhibited no differences in average energy-corrected milk yield, ADG, or BW. The correlation between RFILac100DIM and RFILac-Full was 0.72. Thus, RFIGrowth may serve as an indicator trait for RFI during lactation, and selection for heifers exhibiting low RFIGrowth should improve overall herd feed efficiency during lactation. Correlation analysis between RFILac-Full (10 to 305 DIM) and subperiod estimates of RFI during lactation indicated a test period of 64 to 70 d in duration occurring between 150 to 220 DIM provided a reliable approximation (r ≥ 0.90) of RFILac-Full among the test periods evaluated.

Increasing the number of single nucleotide polymorphisms used in genomic evaluation of dairy cattle.

GeneSeek (Neogen Corp., Lexington, KY) designed a new version of the GeneSeek Genomic Profiler HD BeadChip for Dairy Cattle, which originally had >77,000 single nucleotide polymorphisms (SNP). A set of >140,000 SNP was selected that included all SNP on the existing GeneSeek chip, all SNP used in US national genomic evaluations, SNP that were possible functional mutations, and other informative SNP. Because SNP with a lower minor allele frequency might track causative variants better, 30,000 more SNP were selected from the Illumina BovineHD Genotyping BeadChip (Illumina Inc., San Diego, CA) by choosing SNP to maximize differences in minor allele frequency between a SNP being considered for the new chip and the 2 SNP that flanked it. Single-gene tests were included if their location was known and bioinformatics indicated relevance for dairy cattle. To determine which SNP from the new chip should be included in genomic evaluations, genotypes available from chips already in use were used to impute and evaluate the SNP set. Effects for 134,511 usable SNP were estimated for all breed-trait combinations; SNP with the largest absolute values for effects were selected (5,000 for Holsteins, 1,000 for Jerseys, and 500 each for Brown Swiss and Ayrshires for each trait). To increase overlap with the 60,671 SNP currently used for genomic evaluation, 12,094 more SNP with the largest effects were added. After removing SNP with many parent-progeny conflicts, 84,937 SNP remained. Three cutoff studies were conducted with 3 SNP sets to determine reliability gain over that for parent average when evaluations based on August 2011 data were used to predict December 2014 performance. Across all traits, mean Holstein reliability gains were 32.5, 33.4, and 32.0 percentage points for 60,671, 84,937, and 134,511 SNP, respectively. After genotypes from the new chip became available, the proposed set was reduced from 84,937 to 77,321 SNP to remove SNP that were not included during manufacture, reduce computing time, and improve imputation performance. The set of 77,321 SNP was evaluated using August 2011 data to predict April 2015 performance. Reliability gain over 60,671 SNP was 1.4 percentage points across traits for Holsteins. Improvement over 84,937 SNP was partially the result of 4mo of additional data and genotypes from the new chip. Revision of the SNP set used for genomic evaluation is expected to be an ongoing process to increase evaluation accuracy.

Effect of reference population size and available ancestor genotypes on imputation of Mexican Holstein genotypes.

The effects of reference population size and the availability of information from genotyped ancestors on the accuracy of imputation of single nucleotide polymorphisms (SNP) were investigated for Mexican Holstein cattle. Three scenarios for reference population size were examined: (1) a local population of 2,011 genotyped Mexican Holsteins, (2) animals in scenario 1 plus 866 Holsteins in the US genotype database (GDB) with genotyped Mexican daughters, and (3) animals in scenario 1 and all US GDB Holsteins (338,073). Genotypes from 4 chip densities (2 low density, 1 mid density, and 1 high density) were imputed using findhap (version 3) to the 45,195 markers on the mid-density chip. Imputation success was determined by comparing the numbers of SNP with 1 or 2 alleles missing and the numbers of differently predicted SNP (conflicts) among the 3 scenarios. Imputation accuracy improved as chip density and numbers of genotyped ancestors increased, and the percentage of SNP with 1 missing allele was greater than that for 2 missing alleles for all scenarios. The largest numbers of conflicts were found between scenarios 1 and 3. The inclusion of information from direct ancestors (dam or sire) with US GDB genotypes in the imputation of Mexican Holstein genotypes increased imputation accuracy by 1 percentage point for low-density genotypes and by 0.5 percentage points for high-density genotypes, which was about half the gain found with information from all US GDB Holsteins. A larger reference population and the availability of genotyped ancestors improved imputation; animals with genotyped parents in a large reference population had higher imputation accuracy than those with no or few genotyped relatives in a small reference population. For small local populations, including genotypes from other related populations can aid in improving imputation accuracy.

Genome-wide detection of signatures of selection in Korean Hanwoo cattle.

The Korean Hanwoo cattle have been intensively selected for production traits, especially high intramuscular fat content. It is believed that ancient crossings between different breeds contributed to forming the Hanwoo, but little is known about the genomic differences and similarities between other cattle breeds and the Hanwoo. In this work, cattle breeds were grouped by origin into four types and used for comparisons: the Europeans (represented by six breeds), zebu (Nelore), African taurine (N'Dama) and Hanwoo. All animals had genotypes for around 680 000 SNPs after quality control of genotypes. Average heterozygosity was lower in Nelore and N'Dama (0.22 and 0.21 respectively) than in Europeans (0.26-0.31, with Shorthorn as outlier at 0.24) and Hanwoo (0.29). Pairwise FST analyses demonstrated that Hanwoo are more related to European cattle than to Nelore, with N'Dama in an intermediate position. This finding was corroborated by principal components and unsupervised hierarchical clustering. Using genome-wide smoothed FST , 55 genomic regions potentially under positive selection in Hanwoo were identified. Among these, 29 were regions also detected in previous studies. Twenty-four regions were exclusive to Hanwoo, and a number of other regions were shared with one or two of the other groups. These regions overlap a number of genes that are related to immune, reproduction and fatty acid metabolism pathways. Further analyses are needed to better characterize the ancestry of the Hanwoo cattle and to define the genes responsible to the identified selection peaks.

Use of residual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selection.

Improved feed efficiency is a primary goal in dairy production to reduce feed costs and negative impacts of production on the environment. Estimates for efficiency of feed conversion to milk production based on residual feed intake (RFI) in dairy cattle are limited, primarily due to a lack of individual feed intake measurements for lactating cows. Feed intake was measured in Holstein cows during the first 90 d of lactation to estimate the heritability and repeatability of RFI, minimum test duration for evaluating RFI in early lactation, and its association with other production traits. Data were obtained from 453 lactations (214 heifers and 239 multiparous cows) from 292 individual cows from September 2007 to December 2011. Cows were housed in a free-stall barn and monitored for individual daily feed consumption using the GrowSafe 4000 System (GrowSafe Systems, Ltd., Airdrie, AB, Canada). Animals were fed a total mixed ration 3 times daily, milked twice daily, and weighed every 10 to 14 d. Milk yield was measured at each milking. Feed DM percentage was measured daily, and nutrient composition was analyzed from a weekly composite. Milk composition was analyzed weekly, alternating between morning and evening milking periods. Estimates of RFI were determined as the difference between actual energy intake and predicted intake based on a linear model with fixed effects of parity (1, 2, ≥ 3) and regressions on metabolic BW, ADG, and energy-corrected milk yield. Heritability was estimated to be moderate (0.36 ± 0.06), and repeatability was estimated at 0.56 across lactations. A test period through 53 d in milk (DIM) explained 81% of the variation provided by a test through 90 DIM. Multiple regression analysis indicated that high efficiency was associated with less time feeding per day and slower feeding rate, which may contribute to differences in RFI among cows. The heritability and repeatability of RFI suggest an opportunity to improve feed efficiency through genetic selection, which could reduce feed costs, manure output, and greenhouse gas emissions associated with dairy production.

Technical note: Characteristics and use of the Illumina BovineLD and GeneSeek Genomic Profiler low-density bead chips for genomic evaluation.

The GeneSeek Genomic Profiler (GGP) BeadChip (GeneSeek, Lincoln, NE), which became available commercially in February 2012, is based on the Illumina BovineLD Genotyping BeadChip (Illumina Inc., San Diego, CA), with 1,745 additional single nucleotide polymorphisms (SNP) for genomic evaluation and SNP for proprietary single-gene tests. The BovineLD chip with 6,909 SNP, which replaced the Illumina GoldenGate Bovine3K Genotyping BeadChip, has been available since October 2011. The GGP's additional SNP for genomic evaluation were selected to improve imputation by filling SNP gaps on chromosomes and including more Bovine3K SNP than were on the BovineLD chip and to impute microsatellite alleles to facilitate parentage validation. The SNP for single-gene tests were included to minimize the number of separate tests required for those genes, particularly for bulls. The September 2012 US national genomic evaluation included genotypes from BovineLD and GGP chips for 82,510 animals. For those data, BovineLD and GGP performance was similar. The call rate for SNP on these chips that were used in genomic evaluation was 99.6%. The 9 Y-chromosome SNP in common on the BovineLD and GGP chips were highly effective in sex validation (call rate of 99% for males and 0.01% for females). For both chips, the rate of parent-progeny conflicts on a SNP basis (≤ 0.004%) was similar to that for SNP on the Illumina BovineSNP50 Genotyping BeadChip. Imputation accuracy for 45,187 BovineSNP50 SNP averaged 99.4% for H olsteins. Imputation accuracy was slightly higher for the GGP chip compared with the BovineLD chip because of its additional SNP. Reliability for genomic evaluations using BovineLD and GGP genotypes was 3 percentage points higher than that for Bovine3K genotypes.

Quantitative trait loci for resistance to Haemonchus contortus artificial challenge in Red Maasai and Dorper sheep of East Africa.

A genome-wide scan was performed to detect quantitative trait loci (QTL) for resistance to the gastrointestinal nematode Haemonchus contortus in a double backcross population of Red Maasai and Dorper sheep. The mapping population comprised six sire families, with 1026 lambs in total. The lambs were artificially challenged with H. contortus at about 6.5 months of age, and nine phenotypes were measured: fecal egg count, packed cell volume decline, two weight traits and five worm traits. A subset of the population (342 lambs) was selectively genotyped for 172 microsatellite loci covering 25 of the 26 autosomes. QTL mapping was performed for models which assumed that the QTL alleles were either fixed or segregating within each breed, combined with models with only an additive QTL effect fitted or both additive and dominance QTL effects fitted. Overall, QTL significant at the 1% chromosome-wide level were identified for 22 combinations of trait and chromosome. Of particular interest are a region of chromosome 26 with putative QTL for all nine traits and a region of chromosome 2 with putative QTL for three traits. Favorable QTL alleles for disease resistance originated in both the Red Maasai and Dorper breeds, were not always fixed within breed and had significant dominance effects in some cases. We anticipate that this study, in combination with follow-up work and other relevant studies, will help elucidate the biology of disease resistance.

Identification of quantitative trait loci affecting resistance to gastrointestinal parasites in a double backcross population of Red Maasai and Dorper sheep.

A genome-wide scan for quantitative trait loci (QTL) affecting gastrointestinal nematode resistance in sheep was completed using a double backcross population derived from Red Maasai and Dorper ewes bred to F(1) rams. This design provided an opportunity to map potentially unique genetic variation associated with a parasite-tolerant breed like Red Maasai, a breed developed to survive East African grazing conditions. Parasite indicator phenotypes (blood packed cell volume - PCV and faecal egg count - FEC) were collected on a weekly basis from 1064 lambs during a single 3-month post-weaning grazing challenge on infected pastures. The averages of last measurements for FEC (AVFEC) and PCV (AVPCV), along with decline in PCV from challenge start to end (PCVD), were used to select lambs (N = 371) for genotyping that represented the tails (10% threshold) of the phenotypic distributions. Marker genotypes for 172 microsatellite loci covering 25 of 26 autosomes (1560.7 cm) were scored and corrected by Genoprob prior to qxpak analysis that included Box-Cox transformed AVFEC and arcsine transformed PCV statistics. Significant QTL for AVFEC and AVPCV were detected on four chromosomes, and this included a novel AVFEC QTL on chromosome 6 that would have remained undetected without Box-Cox transformation methods. The most significant P-values for AVFEC, AVPCV and PCVD overlapped the same marker interval on chromosome 22, suggesting the potential for a single causative mutation, which remains unknown. In all cases, the favourable QTL allele was always contributed from Red Maasai, providing support for the idea that future marker-assisted selection for genetic improvement of production in East Africa will rely on markers in linkage disequilibrium with these QTL.

Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle.

The objective of the present study was to evaluate the predictive ability of direct genomic values for economically important dairy traits when genotypes at some single nucleotide polymorphism (SNP) loci were imputed rather than measured directly. Genotypic data consisted of 42,552 SNP genotypes for each of 1,762 Jersey sires. Phenotypic data consisted of predicted transmitting abilities (PTA) for milk yield, protein percentage, and daughter pregnancy rate from May 2006 for 1,446 sires in the training set and from April 2009 for 316 sires in the testing set. The SNP effects were estimated using the Bayesian least absolute selection and shrinkage operator (LASSO) method with data of sires in the training set, and direct genomic values (DGV) for sires in the testing set were computed by multiplying these estimates by corresponding genotype dosages for sires in the testing set. The mean correlation across traits between DGV (before progeny testing) and PTA (after progeny testing) for sires in the testing set was 70.6% when all 42,552 SNP genotypes were used. When genotypes for 93.1, 96.6, 98.3, or 99.1% of loci were masked and subsequently imputed in the testing set, mean correlations across traits between DGV and PTA were 68.5, 64.8, 54.8, or 43.5%, respectively. When genotypes were also masked and imputed for a random 50% of sires in the training set, mean correlations across traits between DGV and PTA were 65.7, 63.2, 53.9, or 49.5%, respectively. Results of this study indicate that if a suitable reference population with high-density genotypes is available, a low-density chip comprising 3,000 equally spaced SNP may provide approximately 95% of the predictive ability observed with the BovineSNP50 Beadchip (Illumina Inc., San Diego, CA) in Jersey cattle. However, if fewer than 1,500 SNP are genotyped, the accuracy of DGV may be limited by errors in the imputed genotypes of selection candidates.

Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms.

The availability of dense single nucleotide polymorphism (SNP) genotypes for dairy cattle has created exciting research opportunities and revolutionized practical breeding programs. Broader application of this technology will lead to situations in which genotypes from different low-, medium-, or high-density platforms must be combined. In this case, missing SNP genotypes can be imputed using family- or population-based algorithms. Our objective was to evaluate the accuracy of imputation in Jersey cattle, using reference panels comprising 2,542 animals with 43,385 SNP genotypes and study samples of 604 animals for which genotypes were available for 1, 2, 5, 10, 20, 40, or 80% of loci. Two population-based algorithms, fastPHASE 1.2 (P. Scheet and M. Stevens; University of Washington TechTransfer Digital Ventures Program, Seattle, WA) and IMPUTE 2.0 (B. Howie and J. Marchini; Department of Statistics, University of Oxford, UK), were used to impute genotypes on Bos taurus autosomes 1, 15, and 28. The mean proportion of genotypes imputed correctly ranged from 0.659 to 0.801 when 1 to 2% of genotypes were available in the study samples, from 0.733 to 0.964 when 5 to 20% of genotypes were available, and from 0.896 to 0.995 when 40 to 80% of genotypes were available. In the absence of pedigrees or genotypes of close relatives, the accuracy of imputation may be modest (generally <0.80) when low-density platforms with fewer than 1,000 SNP are used, but population-based algorithms can provide reasonably good accuracy (0.80 to 0.95) when medium-density platforms of 2,000 to 4,000 SNP are used in conjunction with high-density genotypes (e.g., >40,000 SNP) from a reference population. Accurate imputation of high-density genotypes from inexpensive low- or medium-density platforms could greatly enhance the efficiency of whole-genome selection programs in dairy cattle.

Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada.

Nearly 57,000 single-nucleotide polymorphisms (SNP) genotyped with the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA) were investigated to determine usefulness of the associated SNP for genomic prediction. Genotypes were obtained for 12,591 bulls and cows, and SNP were selected based on 5,503 bulls with genotypes from a larger set of SNP. The following SNP were deleted: 6,572 that were monomorphic, 3,213 with scoring problems (primarily because of poor definition of clusters and excess number of clusters), and 3,649 with a minor allele frequency of <2%. Number of SNP for each minor allele frequency class (> or =2%) was fairly uniform (777 to 1,004). For 5 contiguous SNP assigned to chromosome 7, no bulls were heterozygous, which indicated that those SNP are actually on the nonpseudoautosomal portion of the X chromosome. Another 178 SNP that were not assigned to a chromosome but that had many fewer heterozygotes than expected were also assigned to the X chromosome. Existence of Hardy-Weinberg equilibrium was investigated by comparing observed with expected heterozygosity. For 11 SNP, the observed percentage of heterozygous individuals differed from the expected by >15%; therefore, those SNP were deleted. For 2,628 SNP, the genotype at another SNP was highly correlated (i.e., genotypes were identical for >99.5% of bulls), and those were deleted. After edits, 40,874 SNP remained. A parent-progeny conflict was declared when the genotypes were alternate homozygotes. Mean number of conflicts was 2.3 when pedigree was correct and 2,411 when it was incorrect. The sire was genotyped for >93% of animals. Maternal grandsire genotype was similarly checked; however, because alternate homozygotes could be valid, a conflict threshold of 16% was used to indicate a need for further investigation. Genotyping consistency was investigated for 21 bulls genotyped twice with differences primarily from SNP that were not scored in one of the genotypes. Concordance for readable SNP was extremely high (99.96-100%). Thousands of SNP that were polymorphic in Holsteins were monomorphic in Jerseys or Brown Swiss, which indicated that breed-specific SNP sets are required or that all breeds need to be considered in the SNP selection process. Genotypes from the Illumina BovineSNP50 BeadChip are of high accuracy and provide the basis for genomic evaluations in the United States and Canada.

Distribution and location of genetic effects for dairy traits.

Genetic effects for many dairy traits and for total economic merit are evenly distributed across all chromosomes. A high-density scan using 38,416 single nucleotide polymorphism markers for 5,285 bulls confirmed 2 previously known major genes on Bos taurus autosomes (BTA) 6 and 14 but revealed few other large effects. Markers on BTA18 had the largest effects on calving ease, several conformation traits, longevity, and total merit. Prediction accuracy was highest using a heavy-tailed prior assuming that each marker had an effect on each trait, rather than assuming a normal distribution of effects as in a linear model, or that only some loci have nonzero effects. A prior model combining heavy tails with finite alleles produced results that were intermediate compared with the individual models. Differences between models were small (1 to 2%) for traits with no major genes and larger for heavy tails with traits having known quantitative trait loci (QTL; 6 to 8%). Analysis of bull recessive codes suggested that marker effects from genomic selection may be used to identify regions of chromosomes to search in detail for candidate genes, but individual single nucleotide polymorphisms were not tracking causative mutations with the exception of diacylglycerol O-acyltransferase 1. Additive genetic merits were constructed for each chromosome, and the distribution of BTA14-specific estimated breeding value (EBV) showed that selection primarily for milk yield has not changed the distribution of EBV for fat percentage even in the presence of a known QTL. Such chromosomal EBV also may be useful for identifying complementary mates in breeding programs. The QTL affecting dystocia, conformation, and economic merit on BTA18 appear to be related to calf size or birth weight and may be the result of longer gestation lengths. Results validate quantitative genetic assumptions that most traits are due to the contributions of a large number of genes of small additive effect, rather than support the finite locus model.

Invited review: reliability of genomic predictions for North American Holstein bulls.

Genetic progress will increase when breeders examine genotypes in addition to pedigrees and phenotypes. Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations for 1,759 bulls born from 1999 through 2002. Genotypes were generated using the Illumina BovineSNP50 BeadChip and DNA from semen contributed by US and Canadian artificial-insemination organizations to the Cooperative Dairy DNA Repository. Genomic predictions for 5 yield traits, 5 fitness traits, 16 conformation traits, and net merit were computed using a linear model with an assumed normal distribution for marker effects and also using a nonlinear model with a heavier tailed prior distribution to account for major genes. The official parent average from 2003 and a 2003 parent average computed from only the subset of genotyped ancestors were combined with genomic predictions using a selection index. Combined predictions were more accurate than official parent averages for all 27 traits. The coefficients of determination (R(2)) were 0.05 to 0.38 greater with nonlinear genomic predictions included compared with those from parent average alone. Linear genomic predictions had R(2) values similar to those from nonlinear predictions but averaged just 0.01 lower. The greatest benefits of genomic prediction were for fat percentage because of a known gene with a large effect. The R(2) values were converted to realized reliabilities by dividing by mean reliability of 2008 daughter deviations and then adding the difference between published and observed reliabilities of 2003 parent averages. When averaged across all traits, combined genomic predictions had realized reliabilities that were 23% greater than reliabilities of parent averages (50 vs. 27%), and gains in information were equivalent to 11 additional daughter records. Reliability increased more by doubling the number of bulls genotyped than the number of markers genotyped. Genomic prediction improves reliability by tracing the inheritance of genes even with small effects.

Environmental sampling to predict fecal prevalence of Salmonella in an intensively monitored dairy herd.

Although dairy cattle are known reservoirs for salmonellae, cattle that are shedding this organism are often asymptomatic and difficult to identify. A dairy herd that was experiencing a sustained, subclinical outbreak of Salmonella enterica subsp. enterica Cerro was monitored for 2 years. Fecal samples from the lactating cows were collected every 6 to 8 weeks and tested for the presence of Salmonella. Fecal prevalence of Salmonella fluctuated throughout the observation period and ranged from 8 to 88%. Manure composites and water trough samples were collected along with the fecal samples, and bulk milk and milk filters were cultured for the presence of Salmonella on a weekly basis. Over 90% of the manure composites--representing high-animal-traffic areas-were positive at each sampling. Salmonella was detected in 11% of milk samples and in 66% of the milk filters. Results of weekly bulk milk quality testing (i.e., bulk tank somatic cell score, standard plate count, preliminary incubation count) were typically well within acceptable ranges. Milk quality variables had low correlations with herd Salmonella fecal prevalence. When observed over time, sampling period average prevalence of Salmonella in milk filters closely paralleled fecal prevalence of Salmonella in the herd. Based on results of this study, milk filters appear to be an effective method for monitoring shedding prevalence at the herd level. In-line filter testing is also a more sensitive measure of Salmonella, and perhaps other pathogens, in raw milk than testing the milk alone.

Detection of germline and somatic copy number variations in cattle.

As a complement to the Bovine HapMap Consortium project, we initiated a systematic study of the copy numbervariation (CNV) within the same cattle population using array comparative genomic hybridization (array CGH). Oligonucleotide CGH arrays were designed and fabricated to cover all chromosomes with an average interval of 6 kb using the latest bovine genome assembly. In the initial screening, three Holstein bulls were selected to represent major paternal lineages of the Holstein breed with some maternal linkages between these lines. Dual-label hybridizations were performed using either Hereford L1 Dominette 01449 or L1 Domino 99375 as reference. The CNVs were represented by gains and losses of normalized fluorescence intensities relative to the reference. The data presented here, for the first time, demonstrated that significant amounts of germline and fewer somatic CNVs exist in cattle, that many CNVs are common both across diverse cattle breeds and among individuals within a breed, and that array CGH is an effective tool to systematically detect bovine CNV. Selected CNVs have been confirmed by independent methods using real-time (RT) PCR. The strategy used in this study, based on genome higher-orderarchitecture variation, is a powerful approach to generating resources for the identification of novel genomic variation and candidate genes for economically important traits.

Characterization of a novel microdeletion polymorphism on BTA5 in cattle.

We present a detailed breakpoint mapping and population frequency analysis of a 214-kb microdeletion that removes multiple olfactory receptor genes. Using progressive rounds of PCR assays, we mapped the upstream and downstream breakpoints of this microdeletion event to approximately 1 and 12 kb genomic regions, respectively. We developed PCR-based genotyping assays, characterized a dairy cattle panel of 96 samples and found that the frequency of the deletion allele was over 51%. Our results indicated that this microdeletion is an ancient event occurring in one of the earlier founders, and that it has been stably inherited across generations in the North American dairy cattle population.